The objective of this research project is to apply advanced methods of mathematical statistical analysis to the processing, interpretation and diagnostic classification of the body surface electrocardiogram. Emphasis is placed on optimal clinical laboratory validation, such as serial section autopsy of the ventricles, coronary arteriography, and cardiac catheterization. It is believed that imitation of the physician's method of visual inspection of the electrocardiogram is not a promising approach for computer techniques. There are two principal reasons for this: (1) Numerous investigations have shown the high diagnostic misclassification rate, even by expert cardiologists; (2) The strength of the computer analysis is based not so much on the recognition of wave morphologies as on a multi-demensional cluster density analysis. Our investigations have shown that advanced methods of data preprocessing, especially of selective averaging, are essential to avoid misclassification due to artifacts. Three methods of statistical analysis are now under investigation: (1) a ranked variate analysis in six dimensional space, (2) a discriminate analysis (Duncan-Walker method), and (3) non-parametric nearest neighbor rule procedure. In the material so far studied (i.e., 1600 normal subjects and 1600 patients) the ranked variate analysis has proven to be superior in relating to the electrophysiological events. Discriminate analysis appears to be a powerful tool for a quantitative assessment of variability and for establishment of reliable probability statements in terms of likelihood ratios for the respective disease categories.